Electricity Consumption Prediction of Solid Electric Thermal Storage with a Cyber–Physical Approach

Huichao Ji, Junyou Yang, Haixin Wang, Kun Tian, Martin Onyeka Okoye and Jiawei Feng
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Huichao Ji: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Junyou Yang: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Haixin Wang: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Kun Tian: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Martin Onyeka Okoye: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Jiawei Feng: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China

Energies, 2019, vol. 12, issue 24, 1-18

Abstract: This paper proposes a cyber–physical approach to enhance the prediction accuracy of electricity consumption of solid electric thermal storage (SETS) system, which integrates a physical model and a data-based cyber model. In the cyber–physical model, the prediction error of the physical model is used as an input of the cyber model to further calibrate the prediction error. Firstly, customers’ behavior characteristics are extracted by the integration of K-means and one-versus-one support vector machine. Secondly, based on the behavior characteristics and ambient temperature, the physical model is developed to predict daily electricity consumption. Finally, the error levels of physical model are classified, together with the temperature and prediction values of the physical model, are selected as the inputs of the cyber model using the back propagation (BP) neural network to calibrate the results of the physical model. The effectiveness of the proposed cyber–physical model (CPM) is verified by a 1 MW SETS system. The simulation results show that, compared with the physical model (PM) and cyber model (CM), the maximum relative errors (MRE) with the CPM are reduced to 25.4% and 4.8%, respectively.

Keywords: solid electric thermal storage; cyber–physical model; K-means; support vector machine; neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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